System and Method for Analyzing a Sensory Stream Using Reservoir Computing

ABSTRACT

The present inventors have recognized that proper utilization of reconfigurable event driven hardware may achieve optimum power conservation in energy constrained environments including a low power general purpose primary processor and one or more electronic sensors. Aspects of neurobiology and neuroscience, for example, may be utilized to provide such reconfigurable event driven hardware, thereby achieving energy-efficient continuous sensing and signature reporting in conjunction with the one or more electronic sensors while the primary processor enters a low power consumption mode. Such hardware is event driven and operates with extremely low energy requirements.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under 0702272 awarded bythe National Science Foundation and HR0011-09-C-0002 awarded by theDepartment of Defense/DARPA. The government has certain rights in theinvention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.13/749,854, entitled “Reconfigurable Event Driven Hardware UsingReservoir Computing for Monitoring an Electronic Sensor and Waking aProcessor,” filed on Jan. 25, 2013, which is herein incorporated byreference.

BACKGROUND OF THE INVENTION

The present invention relates to sensory stream analysis, and inparticular, to detection of trigger signatures from sensory streams inenergy constrained environments.

Many electronic systems today rely on continuous sensing of real worldinformation to guide in data collection, storage, analysis, computation,communication, decision making and/or actuation. The sensors and thefirst tier of the sensory processing systems are often deployed inenergy constrained environments. Typically, the raw data stream must bein analyzed by a low power, general purpose primary processor or centralprocessing unit (“CPU”) for temporal and spatial trigger signatures thateither identify that an event of interest has occurred, or, conversely,rule out such an event. Trigger signatures can be detected flexibly insoftware executing on the primary processor by analyzing the raw datastream as it arrives, but this often requires a significant energybudget due to the ongoing use of the primary processor, particularly forcontinuous and/or high volume data streams.

Such continuous sensing applications are becoming increasinglycommonplace for medical, health, and safety monitoring of criticalsensory data streams, such as human electrocardiograph (“EKG”),electroencephalograph (“EEG”), pulse, blood pressure, and patientactivity levels. For example, human sleep characteristics may bemonitored via an EEG monitoring slow wave activity of the brain,including as described in U.S. Pat. No. 8,029,431, “Method and Apparatusfor Promoting Restorative Sleep” to Giulio Tononi, the contents of whichare hereby incorporated by reference.

Continuous sensing applications are also becoming increasinglycommonplace for environmental monitoring, such as emission levels,pollutant concentrations, or seismic data, and in the mobile space,including smart phones, tablets and other mobile computers. Such sensingmay be used to trigger context and location aware computation and/orcommunication. For example, information sent from a 3-axis accelerometerin a mobile phone may be used to infer the type and level of activity ofthe user, either independently or in conjunction with other sensors. Forexample, entering the driver's seat of an automobile could generate anidentifiable accelerometer signature which could be used to disable textmessaging while driving. The ability to continuously deploy flexiblesensing and processing for these and other applications has thepotential to revolutionize these fields. However, energy constraintsoften limit the development of such applications.

Typically, within the context of energy constrained battery-operatedsystems, the primary processor that interfaces with and controls varioussensors is designed to operate mainly for bursts of user activity. Tooptimize power dissipation and to improve the battery life of thesystem, such processors compute aggressively for short durations, whichconsume a significant amount of power, then enter a sleep/idle or lowpower consumption mode to save energy. Such designs rely on theassumption that periods of sleep will be significantly longer ascompared to the periods of aggressive processing. This assumption doesnot hold true, however, for many continuous sensing applications. Sincecontinuous sensing applications require the primary processor tocontinuously monitor the sensory streams, these applications expect theprocessor to be in the processing mode all the time, thereby causing theprocessor to continuously dissipate significant energy. Furthermore, therequirement of continuously or periodic sampling prevents the primaryprocessor from going into the low power consumption mode which preventsthe opportunity for saving further energy. This often drainsbattery-operated processing systems faster than would otherwise be thecase.

Some designs have advocated using a low-power microcontroller interfacedwith the primary processor to allow continuous sensing, which may be aseparate physical chip or a separate core within the primary processor.Though helpful, such low-power implementations typically include adedicated CPU and various memories and data paths, which still requirefar more energy, physical space and compute resources than necessary forcontinuously analyzing sensory stream data with the least amount ofenergy consumed.

SUMMARY OF THE INVENTION

The present inventors have recognized that proper utilization ofreconfigurable event driven hardware which consumes very low powerrelative to the system, including with respect to the electronic sensor,without the overhead of a low-power microcontroller, may achieve optimumpower conservation in such energy constrained environments. Accordingly,the present inventors have found that aspects of neurobiology andneuroscience may be utilized to provide reconfigurable event drivenhardware achieving such energy-efficient continuous sensing andsignature reporting in conjunction with one or more sensors and aprimary processor. Such hardware is event driven and operates withextremely low energy requirements.

As described above, continuous sensing applications may include, but arenot limited to, EKG, EEG, pulse, blood pressure, patient activity,environmental monitoring emission levels, pollutant concentrations,seismic data, military and healthcare, safety monitoring and mobileconsumer devices. Devices deployed in such domains have access tosensors including, but not limited to, accelerometers, ambient lightsensors, temperature sensors, humidity sensors, pressure sensors,microphones, imagers, optical proximity, touch sensors, low-power radiodevices, gyroscopes, orientation sensors, EEG/EKG sensors, bloodpressure sensors, pulse sensors, chemical sensors, breathalyzers,contaminant sensors, smoke/carbon monoxide sensors, radiation sensors,proximity sensors, and many more.

Reservoir computing is a known framework for computation like a neuralnetwork. Typically an input signal is fed into a fixed (random)dynamical system called reservoir and the dynamics of the reservoir mapthe input to a higher dimension. Then a simple readout mechanism istrained to read the state of the reservoir and map it to the desiredoutput. The main benefit is that the training is performed only at thereadout stage and the reservoir is fixed. “Liquid State Machines” and“Echo State Networks” are two major types of reservoir computing. Suchnetworks may be composed of digitally implemented leaky“integrate-and-fire” neurons operating in a clocked manner. In anembodiment, reconfigurable event driven hardware may be implemented, atleast in part, using a neural network.

The proposed reconfigurable event driven hardware enables extremelylow-power continuous monitoring of the aforementioned sensors withindifferent applications by offloading the sampling and signaturedetection tasks from the primary processor within a device having accessto one or more sensors. This reconfigurable hardware interfaces andinteracts with the available sensors and continuously monitors theirstates and invokes the primary processor when a trigger signature isdetected and further processing is necessary. This saves a significantamount of energy because, first, the primary processor can spend littleto no power sampling the sensors, second, the primary processor mayfrequently enter a sleep/idle or low power consumption mode, and third,since the reconfigurable hardware is event driven, it will dissipatepower primarily when it receives a sample of data from the availablesensors.

In an embodiment, sensors are interfaced with a fully reconfigurableevent driven hardware that continuously monitors the state of thesensors and only invokes the primary processor when a trigger signatureis identified. The reconfigurable event driven hardware can beimplemented as a separate integrated circuit chip or as a low-powercompute resource within the primary processor. The energy constrainedprimary processor or a separate processor can configure the event drivenhardware to monitor any number of sensors and any kind of spatial ortemporal trigger signatures. The event driven hardware can also bereconfigured to detect trigger signatures that meet the demands of anyuser level application. Furthermore, if any application demands change,the hardware can be reconfigured during runtime to account for thosechanges. The event driven hardware can also be configured toconcurrently monitor multiple sensors which allow the hardware toidentify trigger signatures across a wide variety of sensory modalities.

These particular objects and advantages may apply to only someembodiments falling within the claims and thus do not define the scopeof the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of exampleonly, with reference to the accompanying drawings, in which:

FIG. 1 is a logical diagram of a system with reconfigurable event drivenhardware that continuously monitors the state of a plurality of sensorsand invokes a primary processor when a trigger signature is identifiedthereby maximizing energy efficiency in accordance with an embodiment ofthe present invention;

FIG. 2 is an architectural diagram of the system of FIG. 1, includingpower distribution and communication with software and related modules,in accordance with an embodiment of the present invention;

FIG. 3 is a logical diagram illustrating reconfigurable event drivenhardware implemented using a reservoir computing, in accordance with anembodiment of the present invention;

FIG. 4 is a logical diagram illustrating an exemplary embodiment of thepresent invention in which the primary processor, the electronic sensorand the reconfigurable event driven hardware are integrated within thesame enclosure;

FIG. 5 is a logical diagram illustrating an exemplary embodiment of thepresent invention in which the primary processor and the reconfigurableevent driven hardware are integrated within the same enclosure, and theelectronic sensor is in a separate, external enclosure; and

FIG. 6 is a logical diagram illustrating an exemplary embodiment of thepresent invention in which the reconfigurable event driven hardware andthe electronic sensor are integrated within the same enclosure, and theprimary processor is in a separate, external enclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring to FIG. 1, the present invention shall be described in thecontext of an electronic system 10 comprising a primary processor 12coupled to a reconfigurable event driven hardware 14. The primaryprocessor 12 may be low power, general purpose processor or CPU adaptedto execute software. The primary processor 12 and the reconfigurableevent driven hardware 14 are each coupled, in turn, to one or moredigital sensors 16 and to one or more analog to digital converters(“ADC”) 18. The ADC 18 is, in turn, coupled to one or more analogsensors 20.

The digital sensors 16 may include, for example, an accelerometer,compass, pressure sensor, temperature sensor or any other sensoramenable to digital sensing and sampling a condition as understood inthe art. The analog sensors 20 may include, for example, a gyroscope, anEKG, an EEG or any other sensor amenable to analog sensing and samplingof a condition as understood in the art. Upon sensing real worldinformation, the digital sensors 16 outputs a raw digital data stream 30to the primary processor 12 and a raw digital data stream 32 to thereconfigurable event driven hardware 14. Similarly, upon sensing realworld information, the analog sensors 20 output a raw analog data stream40 to the ADC 18, which, in turn, digitally samples the raw analog datastream and produces a sampled digital data stream 42 coupled the primaryprocessor 12 and a sampled digital data stream 44 coupled to thereconfigurable event driven hardware 14.

The digital sensors 16 and/or the analog sensors 20 may be adapted tocontinuously provide an output that reflects presently sensed conditionsfrom each. The primary processor 12 may, in turn, continuously monitorthe output data streams from each sensor, and may identify predeterminedsensed conditions, or trigger signatures, from either. Triggersignatures may be spatial and/or temporal in nature as understood in theart, such as triggering upon reaching a particular acceleration and/or aparticular period of time. Upon a trigger signature occurring, theprimary processor 12 may invoke appropriate software routines toinitiate any actions required in response to the identified triggersignatures.

The primary processor 12 can configure the reconfigurable event drivenhardware 14 via a reconfiguration data path 50 to similarly continuouslymonitor the output data streams from the digital sensors 16 and/or theanalog sensors 20 and similarly identify trigger signatures from either.Once configured, the reconfigurable event driven hardware 14continuously monitors the output data streams and looks for triggersignatures. If the reconfigurable event driven hardware 14 detects atrigger signature, it invokes primary processor 12 through the controldata path 52 and communicates the trigger signature details via atrigger data path 54 to the primary processor 12. At this point theprocessor 12 can invoke appropriate software routines to initiate anyactions required in response to the identified trigger signature.Accordingly, the primary processor 12 may enter a sleep/idle or lowpower consumption mode, thereby saving overall energy in the system,while the reconfigurable event driven hardware 14 continues to monitorthe sensors for trigger signatures.

In an alternative embodiment, data streams to the primary processor 12and/or the reconfigurable event driven hardware 14 may be combined orotherwise partitioned. In addition, data paths between the primaryprocessor 12 and the reconfigurable event driven hardware 14 may besimilarly combined or otherwise partitioned.

Referring now to FIG. 2, a detailed architectural diagram of the systemof FIG. 1, including power distribution and communication with softwareand related modules, is shown in accordance with an embodiment of thepresent invention. A power source 201 coupled to a power distributionmodule 202 provides power to various modules in the system, includingthe primary processor 204, the reconfigurable event driven hardware 206,the analog sensors 208 and the digital sensors 210.

The reconfigurable event driven hardware 206 interacts with the primaryprocessor 204 through the processor power management module 212 and theprocessor reconfigurable hardware interface module 214. An applicationmodule 216 in need of continuous sensing capability makes a request tothe operating system (“OS”) module 218 and provides details about thesensors to be monitored and signatures to be identified. Thisinformation is in turn communicated to the primary processor 204 whichconfigures the reconfigurable event driven hardware 206 with appropriateparameters via the processor reconfigurable hardware interface module214.

At this point, the primary processor 204 can go into sleep/idle or a lowpower consumption mode to conserve power while the reconfigurable eventdriven hardware 206 sets up links with the appropriate sensors andcontinuously monitors for the trigger signatures. In the case when thereconfigurable event driven hardware 206 identifies a trigger signature,it communicates with the processor power management module 212 todetermine the present state of the primary processor 204. If the primaryprocessor 204 is in a sleep/idle or a low power consumption mode, thereconfigurable event driven hardware 206 requests the processor powermanagement module 212 to bring the primary processor 204 into an activestate, then details of the identified trigger signatures arecommunicated to the primary processor 204 via the processorreconfigurable hardware interface module 214.

Upon receiving the details of the trigger signature, the primaryprocessor 204 sends an interrupt to the OS module 218 communicating thepresent trigger signature occurrence, then invokes the continuoussensing application module 216. The application module 216 can thendynamically invoke software routines to respond to the specificsignature. Based on the behavior coded in the invoked software routines,the application module 216 may request an action in to the triggersignature. This action may be realized, for example, via interactionwith a user an actuation module 220, such as producing a sound, or userinterface module 222, such a displaying to a screen.

Referring now to FIG. 3, a logical diagram illustrating reconfigurableevent driven hardware 302 implemented using reservoir computing, isshown in accordance with an embodiment of the present invention. Thereconfigurable event driven hardware 302 is coupled to a primaryprocessor 304, and each of which are coupled to an electronic sensor306, which may be one or more analog and/or digital sensors, asdescribed above. In the mobile phone space, the electronic sensor 306may include common sensors available such as accelerometers, gyroscopes,microphones, cameras, etc. For energy constrained medical applications,the electronic sensor 306 may include temperature sensors, pressuresensors, EEG sensors, EKG sensors, etc.

To achieve continuous sensing and trigger signature detection in theenergy constrained environment, it is important that the reconfigurableevent driven hardware 302 operate in an event driven manner. In otherwords, the occurrence of a particular spatial and/or temporal signature,sensed by the electronic sensor 306, subsequently drives a particulartrigger.

It is also important that a capturing element 308 of the reconfigurableevent driven hardware 302 has direct inputs from the electronic sensor306, without requiring input from or the operation of the primaryprocessor 304. Energy savings are achieved by the low power event drivenoperation of the reconfigurable event driven hardware 302 paired withthe fact that primary processor 304 can operate in a sleep/idle or lowpower consumption mode until awoken by the reconfigurable event drivenhardware 302.

The reconfigurable event driven hardware 302 may comprise the capturingelement 308 and a detecting and/or classifying element 310. To achieveoptimum power conservation in the energy constrained environment, in apreferred embodiment, the reconfigurable event driven hardware 302 maybe implemented as a neural network, without the overhead of a low-powermicrocontroller, for example. Accordingly, a portion of thereconfigurable event driven hardware 302, such as capturing element 308,may be configured as an implementation of an “Echo State Network,” or“Liquid State Machine,” to capture the temporal behavior of theelectronic sensor 306. Echo State Networks are described, for example,in “Real-Time Computing Without Stable States: A New Framework forNeural Computation Based on Perturbations,” Maass, Wolfgang;Natschliger, Thomas; Markram, Henry (November 2002), and “ComputationalModels for Generic Cortical Microcircuits,” Maass, Wolfgang;NatschlAger, Thomas (2003), the contents of each of which are herebyincorporated by reference. In alternative embodiments, shift registersand/or finite state machines may be used in addition to or insubstitution of an Echo State Network and/or Liquid State Machine.

Another portion of the reconfigurable event driven hardware 302, such asthe detecting and/or classifying element 310, may be configured as a“Multi-Layered Perception Network” for detecting and/or classifyingparticular trigger signatures. Multi-Layered Perception Networks aredescribed, for example, in “Artificial Intelligence: A Modem Approach,”2nd ed., Russell, Stuart J. and Norvig, Peter, Upper Saddle River, N.J.,Prentice Hall (2003), the contents of which are hereby incorporated byreference.

Both the Echo State Network and the Multi-Layered Perceptron Network maybe composed of digitally implemented “leaky integrate-and-fire neurons”operating in a clocked manner. The leaky integrate-and-fire neuroncontains an internal state variable known as the membrane potential. Onany particular cycle, each of the leaky integrate-and-fire neurons sumsits inputs, adds them to the current membrane potential of the neuron,and subtracts a leakiness factor. If the membrane potential exceeds athreshold value, the neuron fires, and the firing becomes the input toother neurons in the following cycle. Afterwards, the membrane potentialvalue is set to a reset value.

The neurons may utilize a small set of configurable parameters in orderto achieve the desired leaky integrate-and-fire behavior. These includethe membrane threshold value parameter, a positive (excitatory)connectivity strength parameter, a negative (inhibitory) connectivitystrength parameter and a membrane leak value parameter. In oneembodiment, for example, the threshold value may be an 8-bit value, thepositive (excitatory) connectivity strength may be a 6-bit value, thenegative (inhibitory) connectivity strength may be a 6-bit value and themembrane leak may be a 6-bit value.

The Echo State Network may be composed of a variable number of leakyintegrate-and-fire neurons. These leaky integrate-and-fire neuronscommunicate with each other utilizing a random and sparse connectivityscheme.

As described above, the outputs of the electronic sensor 306 serve asthe inputs to the capturing element 308 which may be an Echo StateNetwork. In alternative embodiments, the outputs of the electronicsensor 306 could be buffered, applied to threshold and/or connecteddirectly to a small subset of the leaky integrate-and-fire neurons inthe Echo State Network, such as an input layer of neurons 312 of thecapturing element 308. The random sparse connectivity of the Echo StateNetwork embodied in the capturing element 308 allows these activationsto propagate among the recurrently connected leaky integrate-and-fireneurons over time. As new inputs from the electronic sensor 306 activatethe input layer of neurons 312, the state of the Echo State Networkembodied in the capturing element 308 captures both the present sensoryinputs as well as the sensory inputs of the recent past. This behavioris where the Echo State Network derives its name. That is, a snapshot ofthe Echo State Network describes the present input as well as the“echoes” remaining from previous sensory inputs. The input layer ofneurons 312 may feed forward to an intermediate layer of neurons 314,which may, in turn, feed forward to an output layer of neurons 316,which may, in turn, couple to the detecting and/or classifying element310.

The Multi-Layered Perceptron Network embodied in the detecting and/orclassifying element 310 may also be composed of a variable number ofleaky integrate-and-fire neurons. The Multi-Layered Perceptron Networkis used to characterize and classify the trigger signatures that havebeen captured by the Echo State Network embodied in the capturingelement 308. That is, the temporal activity of the electronic sensor 306is first captured by the Echo State Network embodied in the capturingelement 308, and then the Multi-Layered Perceptron Network embodied indetecting and/or classifying element 310 detects trigger signatures byclassifying the behavior of the Echo State Network.

The connectivity of the Multi-Layered Perceptron Network embodied in thedetecting and/or classifying element 310 must be configured for theparticular trigger signatures of interest. This connectivity may firstbe configured in software based on prototype trigger signatures providedby the user. The Multi-Layered Perceptron Network may comprise an inputlayer of neurons 318, which may, in turn, feed forward to anintermediate layer of neurons 320, which may, in turn, feed forward toan output layer of neurons 322, which may, in turn, couple to theprimary processor 304.

The output layer of neurons 322 may then determine the handling andpower mode of the overall energy constrained system. For example, aparticular neuron in the output layer of neurons 322 may detect atrigger signature based on accelerometer data that indicates a user hasentered the driver seat of the car. The output layer of neurons 322 maythen can wake up the primary processor 304 and indicate that this eventhas occurred, and the primary processor 304 may, in turn, take theappropriate actions associated with this trigger signature.

As described above, the computation the leaky integrate-and-fire neuronperforms is based on its parameterized membrane leak, the electronicsensor 306 and other connected leaky integrate-and-fire neurons. Areconfigurable event driven interconnect 330 embodied in thereconfigurable event driven hardware 302 allows these leakyintegrate-and-fire neurons to be configured into the various structures,including the Echo State Network embodied in the capturing element 308and the Multi-Layered Perceptron Network embodied in the detectingand/or classifying element 310, and defines the leaky integrate-and-fireneuron parameters.

In preferred embodiment, the Echo State Network embodied in thecapturing element 308 may utilize a sparse connectivity which can bedefined for a particular trigger signature sensing application ofinterest. Within the Echo State Network, the reconfigurable event driveninterconnect 330 uses a sparse and random connectivity, with theprobability of connection proportional to the physical distance betweenthe leaky integrate-and-fire neurons from which it is composed. Theconnectivity of the Multi-Layered Perceptron Network embodied in thedetecting and/or classifying element 310 can be derived based onprototype trigger signature examples input to the Echo State Network.

Finally, the reconfigurable event driven interconnect 330 can beconfigured so the leaky integrate-and-fire neurons of the Echo StateNetwork embodied in the capturing element 308 serve as the inputs to theMulti-Layered Perceptron Network embodied in the detecting and/orclassifying element 310. In an embodiment, the Echo State Network maycomprise, for example, about 216 leaky integrate-and-fire neurons, withsix input layer neurons connected to 36 neurons, operating in a clockedmanner.

A software development environment may be used to allow theconfiguration of the reconfigurable event driven hardware 302 forvarious trigger signature detections. The software developmentenvironment may run on the primary processor 304, or from anothergeneral purpose processor or CPU capable of running the softwaredevelopment tools that can be interfaced with the reconfigurable eventdriven hardware 302. The software may be used to configure thereconfigurable event driven hardware 302 as a number of leakyintegrate-and-fire neurons, and via the reconfigurable event driveninterconnect 330, with a topology necessary to implement an Echo StateNetwork and Multi-Layered Perceptron Network.

The software may also be used to set the values for the variousparameters of the leaky integrate-and-fire neurons and the connectivitystructures as described above.

The software may also utilize trigger signatures to automaticallyconfigure the reconfigurable event driven interconnect 330 and thereconfigurable event driven hardware 302. For example, detecting aperson entering the driver seat of an automobile based on accelerometerdata may be of high importance. A developer could collect a number ofpredetermined trigger signatures based on collected accelerometer datawhich may be used in conjunction with the software developmentenvironment to configure the reconfigurable event driven interconnect330 and the reconfigurable event driven hardware 302. Here, a softwaremodel of the Echo State Network and Multi-Layered Perceptron Network maybe initialized, but the configuration of the reconfigurable event driveninterconnect 330 may not yet be known. The predetermined triggersignatures may be used as inputs to the software model of thereconfigurable event driven hardware 302, and the connectivity of theMulti-Layered Perceptron is learned using back-propagation, a trainingmethod common to software neural network implementations. Once thereconfigurable event driven hardware 302 has been properly trained forthe necessary set of trigger signatures, the software developmentenvironment may be used to configure the actual hardware.

An example of how the reconfigurable event driven hardware 302 canprovide energy efficient continuous sensing is in the context ofdetecting gestures based on mobile phone accelerometer data. Here, theelectronic sensor 306 may be a 3-axis accelerometer common in manymobile phone devices. Using the software development environmentdescribed above, an Echo State Network composed, for example, of about216 leaky integrate-and-fire neurons may be used to capture the currentand previous sensory data from the 3-axis accelerometer. In oneembodiment, for example, a Multi-Layered Perceptron Network may beconfigured of about 227 leaky integrate-and-fire neurons, with three ofthese neurons being dedicated as output layer neurons. Using thesoftware development environment along with prototype triggersignatures, the Multi-Layered Perceptron Network may be trained viaback-propagation to distinguish between three particular gestures.

Using this general approach, the reconfigurable event driven hardware302 could be further configured to recognize any number of distinctgestures based on accelerometer data. Accordingly, the rest of themobile phone could be powered down into a sleep/idle or low powerconsumption mode, until a particular trigger signature gesture wasdetected to reactivate the device.

Referring now to FIG. 4, a logical diagram illustrating an exemplaryembodiment of the present invention is shown. A reconfigurable eventdriven hardware 402 is coupled to a primary processor 404, each of whichis coupled to an electronic sensor 406. Here, the reconfigurable eventdriven hardware 402, the primary processor 404 and the electronic sensor406 are integrated within a single enclosure 408, which is the energyconstrained device.

Referring now to FIG. 5, a logical diagram illustrating anotherexemplary embodiment of the present invention is shown. Similarly, areconfigurable event driven hardware 502 is coupled to a primaryprocessor 504, and each of which is coupled to an electronic sensor 506.However, here the reconfigurable event driven hardware 502 and theprimary processor 504 are integrated within a single enclosure 508, andthe electronic sensor 506 is in a separate. external enclosure 510.

Referring now to FIG. 6, a logical diagram illustrating yet anotherexemplary embodiment of the present invention is shown. Similarly, areconfigurable event driven hardware 602 is coupled to a primaryprocessor 604, each of which is coupled to an electronic sensor 606.However, here the reconfigurable event driven hardware 602 and theelectronic sensor 606 are integrated within a single enclosure 610, andthe primary processor 604 is in a separate, external enclosure 608. Itmay now be appreciated that there are many integration possibilities forinterfacing reconfigurable event driven hardware with a primaryprocessor and available sensors that enable low-power continuoussensing.

One or more specific embodiments of the present invention have beendescribed above. It is specifically intended that the present inventionnot be limited to the embodiments and/or illustrations contained herein,but include modified forms of those embodiments including portions ofthe embodiments and combinations of elements of different embodiments ascome within the scope of the following claims. It should be appreciatedthat in the development of any such actual implementation, as in anyengineering or design project, numerous implementation-specificdecisions must be made to achieve the developers' specific goals, suchas compliance with system-related and business related constraints,which may vary from one implementation to another. Moreover, it shouldbe appreciated that such a development effort might be complex and timeconsuming, but would nevertheless be a routine undertaking of design,fabrication, and manufacture for those of ordinary skill having thebenefit of this disclosure. Nothing in this application is consideredcritical or essential to the present invention unless explicitlyindicated as being “critical” or “essential.”

Certain terminology is used herein for purposes of reference only, andthus is not intended to be limiting. For example, terms such as “upper,”“lower,” “above,” and “below” refer to directions in the drawings towhich reference is made. Terms such as “front,” “back,” “rear,”“bottom,” “side,” “left” and “right” describe the orientation ofportions of the component within a consistent but arbitrary frame ofreference which is made clear by reference to the text and theassociated drawings describing the component under discussion. Suchterminology may include the words specifically mentioned above,derivatives thereof, and words of similar import. Similarly, the terms“first,” “second” and other such numerical terms referring to structuresdo not imply a sequence or order unless clearly indicated by thecontext.

When introducing elements or features of the present disclosure and theexemplary embodiments, the articles “a,” “an,” “the” and “said” areintended to mean that there are one or more of such elements orfeatures. The terms “comprising,” “including” and “having” are intendedto be inclusive and mean that there may be additional elements orfeatures other than those specifically noted. It is further to beunderstood that the method steps, processes, and operations describedherein are not to be construed as necessarily requiring theirperformance in the particular order discussed or illustrated, unlessspecifically identified as an order of performance. It is also to beunderstood that additional or alternative steps may be employed.

References to “a microprocessor” and “a processor” or “themicroprocessor” and “the processor” can be understood to include one ormore microprocessors that can communicate in a stand-alone and/or adistributed environment(s), and can thus be configured to communicatevia wired or wireless communications with other processors, where suchone or more processor can be configured to operate on one or moreprocessor-controlled devices that can be similar or different devices.Furthermore, references to memory, unless otherwise specified, caninclude one or more processor-readable and accessible memory elementsand/or components that can be internal to the processor-controlleddevice, external to the processor-controlled device, and can be accessedvia a wired or wireless network.

All of the publications described herein including patents andnon-patent publications are hereby incorporated herein by reference intheir entireties.

What is claimed is:
 1. A method for analyzing a sensory stream from anelectronic sensor in an energy-constrained environment, the methodcomprising: (a) configuring a reconfigurable event driven hardware incommunication with the electronic sensor to detect a trigger signaturefrom the sensory stream, wherein the reconfigurable event drivenhardware includes reservoir computing implementing: (i) a capturingelement configured to capture a temporal behavior of the sensory stream;and (ii) a classifying element configured to classify an output of thecapturing element to identify the trigger signature; (b) placing aprocessor in a low power consumption mode while the reconfigurable eventdriven hardware monitors the sensory stream to detect the triggersignature; and (c) after step (b), upon the reconfigurable event drivenhardware detecting the trigger signature, communicating details of thetrigger signature to the processor.
 2. The method of claim 1, furthercomprising using the processor to configure the reconfigurable eventdriven hardware to detect the trigger signature.
 3. The method of claim2, further comprising the reconfigurable event driven hardwarecontinuously monitoring the sensory stream to detect the triggersignature while the processor is in the low power consumption mode. 4.The method of claim 2, further comprising configuring the reconfigurableevent driven hardware via an Application Program Interface (API).
 5. Themethod of claim 4, further comprising the processor invoking a softwareroutine in response to the communication from the reconfigurable eventdriven hardware.
 6. The method of claim 1, wherein the reservoircomputing comprises a Liquid State Machine (LSM) comprised of aplurality of leaky-integrate-and-fire neurons.
 7. The method of claim 6,wherein the reservoir computing further comprises a Multi-LayeredPerceptron Network (MLPN) comprised of a plurality ofleaky-integrate-and-fire neurons.
 8. The method of claim 7, furthercomprising implementing the capturing element using the LSM and theclassifying using the MLPN.
 9. The method of claim 1, furthercomprising, after step (c), deactivating the reconfigurable event drivenhardware and using the processor for continuously monitoring the sensorystream.
 10. The method of claim 1, wherein the electronic sensor is atleast one of an electrocardiograph and an electroencephalograph, andfurther comprising the configuring the reconfigurable event drivenhardware to continuously monitor slow wave activity of a brain.
 11. Themethod of claim 1, further comprising providing the electronic sensorand the reconfigurable event driven hardware in a first enclosure andproviding the processor in a second enclosure separate from the firstenclosure.
 12. The method of claim 1, further comprising providing theprocessor and the reconfigurable event driven hardware in a firstenclosure and providing the electronic sensor in a second enclosureseparate from the first enclosure.
 13. A system for analyzing a sensorystream in an energy-constrained environment comprising: an electronicsensor producing a sensory stream; a processor having a low powerconsumption mode; and a reconfigurable event driven hardware incommunication with the electronic sensor and the processor, thereconfigurable event driven hardware being configured to detect atrigger signature from the sensory stream, wherein the reconfigurableevent driven hardware includes reservoir computing implementing: (i) acapturing element configured to capture a temporal behavior of thesensory stream; and (ii) a classifying element configured to classify anoutput of the capturing element to identify the trigger signature,wherein, upon the reconfigurable event driven hardware detecting thetrigger signature, the reconfigurable event driven hardware is operableto communicate details of the trigger signature to the processor whilethe processor is in the low power consumption mode.
 14. The system ofclaim 13, wherein the processor configures the reconfigurable eventdriven hardware to detect the trigger signature.
 15. The system of claim14, wherein the reconfigurable event driven hardware continuouslymonitors the sensory stream to detect the trigger signature while theprocessor is in the low power consumption mode.
 16. The system of claim13, wherein the reservoir computing comprises a Liquid State Machine(LSM) comprised of a plurality of leaky-integrate-and-fire neurons. 17.The system of claim 16, wherein the reservoir computing furthercomprises a Multi-Layered Perceptron Network (MLPN) comprised of aplurality of leaky-integrate-and-fire neurons.
 18. The system of claim17, wherein the capturing element is implemented by the LSM and theclassifying is implemented by the MLPN.
 19. The system of claim 13,wherein the electronic sensor is at least one of an electrocardiographand an electroencephalograph and the reconfigurable event drivenhardware is configured to continuously monitor slow wave activity of abrain.
 20. The system of claim 13, wherein the reconfigurable eventdriven hardware and one of the electronic sensor and the processor areprovided in a first enclosure, and wherein one of the electronic sensorand the processor not provided in the first enclosure is provided in asecond enclosure separate from the first enclosure.